#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import argparse
import os
import random
import shutil
import time
import warnings

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.distributed.rpc as rpc
import torch.distributed.autograd as dist_autograd
from torch.distributed.optim import DistributedOptimizer

from utils import find_free_port, AverageMeter, ProgressMeter
from pipe_inception import DistInceptionV3

# model_names = sorted(name for name in models.__dict__
#     if name.islower() and not name.startswith("__")
#     and callable(models.__dict__[name]))

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training with Model Parallelism.')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--split-size', default=256, type=int, metavar='N',
                    help='The number of splits when processing a single mini-batch samples.')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=50, type=int,
                    metavar='N', help='print frequency (default: 50)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')


best_acc1 = 0


def save_checkpoint(state, is_best, filename='checkpoint.pth'):
    jobid = os.environ['SLURM_JOBID']
    chk_dir = os.path.join('checkpoint', '{}'.format(jobid))
    if not os.path.isdir(chk_dir):
        os.makedirs(chk_dir)
    filename = f"{chk_dir}/{filename}"
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, f'{chk_dir}/model_best.pth')


def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


def main():
    args = parser.parse_args()

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    # slurm settings.
    import os
    jobid = os.environ['SLURM_JOBID']
    rank = int(os.environ['SLURM_PROCID'])
    world_size = int(os.environ['SLURM_NPROCS'])
    hostfile = f"dist_url_{jobid}.txt"

    if rank == 0:
        import socket
        ip = socket.gethostbyname(socket.gethostname())
        port = find_free_port()
        os.environ['MASTER_ADDR'] = ip
        os.environ['MASTER_PORT'] = f"{port}"
        master_info = f"{ip}:{port}"
        with open(hostfile, 'w') as f:
            f.write(master_info)

        rpc.init_rpc('master', rank=rank, world_size=world_size)
        #start_training(rank, args)
    elif rank == 1:
        import time
        while True:
            if not os.path.exists(hostfile):
                time.sleep(1)
            else:
                break
        with open(hostfile, 'r') as f:
            master_info = f.read()

        ip, port = master_info.split(':')
        print(f"IP: {ip}. Port: {port}")
        os.environ['MASTER_ADDR'] = ip
        os.environ['MASTER_PORT'] = port

        rpc.init_rpc(f"worker{rank}", rank=rank, world_size=world_size)
        start_training(rank, args)

    # block until all rpcs finish.
    rpc.shutdown()


def start_training(rank, args):
    r"""
    The trainer creates a distributed RNNModel and a DistributedOptimizer. Then,
    it performs training on using random input data.
    """
    #model = DistInceptionV3(args.split_size, "worker1")
    model = DistInceptionV3(args.split_size, "master")

    # setup distributed optimizer
    opt = DistributedOptimizer(
        torch.optim.SGD,
        model.parameter_rrefs(),
        lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay
    )
    criterion = torch.nn.CrossEntropyLoss().cuda(0)

    if args.resume:
        if os.path.isfile(args.resume):
            print("loading checkpoint {}".format(args.resume).center(60, '='))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            model.load_state_dict(checkpoint['state_dict'])
            print("Checkpoint loaded (epoch {})".format(args.start_epoch).center(60, '='))
        else:
            print("No checkpoint to be loaded at {}".format(args.resume))

    cudnn.benchmark = True

    train_dir = os.path.join(args.data, 'train')
    val_dir = os.path.join(args.data, 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    train_dataset = datasets.ImageFolder(
        train_dir, transforms.Compose([
            transforms.RandomResizedCrop(299),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    val_data = datasets.ImageFolder(
        val_dir, transforms.Compose([
            transforms.Resize(299),
            transforms.CenterCrop(299),
            transforms.ToTensor(),
            normalize,
        ]))

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=True,
        num_workers=args.workers, pin_memory=True)
    val_loader = torch.utils.data.DataLoader(
        val_data, batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    if args.evaluate:
        validate(val_loader, model, criterion, args)
        return

    for epoch in range(args.start_epoch, args.epochs):
        #adjust_learning_rate(opt, epoch, args)
        print(f"Processing epoch {epoch}...")

        # train for one epoch.
        train(train_loader, model, criterion, opt, epoch, args)
        print(f"End processing epoch {epoch}...")

        # evaluate on validation set.
        if epoch % 5 == 0 or epoch == (args.epochs - 1):
            acc1 = validate(val_loader, model, criterion, args)
            is_best = acc1 > best_acc1
            best_acc1 = max(acc1, best_acc1)

            save_checkpoint({'epoch': epoch + 1,
                             'best_acc1': best_acc1,
                             'state_dict': model.state_dict()},
                            is_best, filename='checkpoin_{}.ckp'.format(rank))


def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(len(train_loader),
                             [batch_time, data_time, losses, top1, top5],
                             prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        if args.gpu is not None:
            images = images.cuda(0, non_blocking=True)
        target = target.cuda(0, non_blocking=True)

        with dist_autograd.context() as context_id:
            # compute output. InceptionV3 will produce a auxiliary output here.
            # Use the solution from: https://github.com/pytorch/vision/issues/302
            # Three loss value solution: loss = loss1 + 0.3 * (loss2 + loss3)
            print(f"Processing sample batch {i}...")
            output, aux_output = model(images)
            loss1 = criterion(output.cuda(0), target)
            loss2 = criterion(aux_output.cuda(0), target)
            loss = loss1 + 0.4 * loss2

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target.cpu(), topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

            # compute gradient and do SGD step
            dist_autograd.backward(context_id, [loss])
            optimizer.step(context_id)

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            progress.display(i)


def validate(val_loader, model, criterion, args):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(len(val_loader), [batch_time, losses, top1, top5],
                             prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            target = target.cuda(0, non_blocking=True)

            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                progress.display(i)

        # TODO: this should also be done with the ProgressMeter
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))

    return top1.avg


if __name__ == '__main__':
    main()
